Early Recognition of Burn- and Trauma-Related Acute Kidney Injury: A Pilot Comparison of Machine Learning Techniques.

Severely burned and non-burned trauma patients are at risk for acute kidney injury (AKI). The study objective was to assess the theoretical performance of artificial intelligence (AI)/machine learning (ML) algorithms to augment AKI recognition using the novel biomarker, neutrophil gelatinase associated lipocalin (NGAL), combined with contemporary biomarkers such as N-terminal pro B-type natriuretic peptide (NT-proBNP), urine output (UOP), and plasma creatinine. Machine learning approaches including logistic regression (LR), k-nearest neighbor (k-NN), support vector machine (SVM), random forest (RF), and deep neural networks (DNN) were used in this study. The AI/ML algorithm helped predict AKI 61.8 (32.5) hours faster than the Kidney Disease and Improving Global Disease Outcomes (KDIGO) criteria for burn and non-burned trauma patients. NGAL was analytically superior to traditional AKI biomarkers such as creatinine and UOP. With ML, the AKI predictive capability of NGAL was further enhanced when combined with NT-proBNP or creatinine. The use of AI/ML could be employed with NGAL to accelerate detection of AKI in at-risk burn and non-burned trauma patients

Automorphisms of Leavitt path algebras: Zhang twist and irreducible representations

In this article, we construct (graded) automorphisms fixing all vertices of Leavitt path algebras of arbitrary graphs in terms of general linear groups over corners of these algebras. As an application, we study Zhang twist of Leavitt path algebras and describe new classes of irreducible representations of Leavitt path algebras of the rose graphs $R_n$ with $n$ petals.Comment: 38 pages. Comments are welcom

Prospective observational study of point-of-care creatinine in trauma.

Background:Patients with trauma are at risk for renal dysfunction from hypovolemia or urological injury. In austere environments, creatinine values are not available to guide resuscitation. A new portable device, the Stat Sensor Point-of-care (POC) Whole Blood Creatinine Analyzer, provides accurate results in <30 s and requires minimal training. This device has not been evaluated in trauma despite the theoretical benefit it provides. The purpose of this study is to determine the clinical impact of the POC device in trauma. Methods:40 patients with trauma were enrolled in a prospective observational study. One drop of blood was used for creatinine determination on the Statsensor POC device. POC creatinine results were compared to the laboratory. Turnaround time (TAT) for POC and laboratory methods was calculated as well as time elapsed to CT scan if applicable. Results:Patients (n=40) were enrolled between December 2014 and March 2015. POC creatinine values were similar to laboratory methods with a mean bias of 0.075±0.27 (p=0.08). Mean analytical TATs for the POC measurements were significantly faster than the laboratory method (11.6±10.0 min vs 78.1±27.9 min, n=40, p<0.0001). Mean elapsed time before arrival at the CT scanner was 52.9±34.2 min. Conclusions:The POC device reported similar creatinine values to the laboratory and provided significantly faster results. POC creatinine testing is a promising development for trauma practice in austere environments and workup of a subset of stable patients with trauma. Further study is warranted to determine clinical impact, both in hospital-based trauma and austere environments

Inverse velocity statistics in two dimensional turbulence

We present a numerical study of two-dimensional turbulent flows in the enstrophy cascade regime, with different large-scale forcings and energy sinks. In particular, we study the statistics of more-than-differentiable velocity fluctuations by means of two recently introduced sets of statistical estimators, namely {\it inverse statistics} and {\it second order differences}. We show that the 2D turbulent velocity field, $\bm u$, cannot be simply characterized by its spectrum behavior, $E(k) \propto k^{-\alpha}$. There exists a whole set of exponents associated to the non-trivial smooth fluctuations of the velocity field at all scales. We also present a numerical investigation of the temporal properties of $\bm u$ measured in different spatial locations.Comment: 9 pages, 12 figure

Common Statistical Concepts in the Supervised Machine Learning Arena

One of the core elements of Machine Learning (ML) is statistics and its embedded foundational rules and without its appropriate integration, ML as we know would not exist. Various aspects of ML platforms are based on statistical rules and most notably the end results of the ML model performance cannot be objectively assessed without appropriate statistical measurements. The scope of statistics within the ML realm is rather broad and cannot be adequately covered in a single review article. Therefore, here we will mainly focus on the common statistical concepts that pertain to supervised ML (i.e. classification and regression) along with their interdependencies and certain limitations

Implementation of High-Sensitivity Cardiac Troponin: Challenges From the International Experience.

ObjectiveImplementation of the newly approved high-sensitivity cardiac troponin (hs-cTn) in the United States presents a challenge for clinical practice. Sex-specific cutoffs, clinical protocols, and workflows will likely require modifications before implementation.MethodsWe conducted a cross-sectional survey of international physicians and laboratorians already utilizing hs-cTn for the evaluation of acute myocardial infarction.ResultsTwenty-two of 54 (41%) eligible participants completed the survey, representing 9 countries and 18 hospitals. All reported successful hs-cTn implementation and diagnostic utility (mean 8.6 + 1.2 out of 10 for best implementation). The major perceived benefit was more rapid evaluation of acute myocardial infarction (14/19, 74%), and the most frequently cited limitation was an increase in the number of measurable hs-cTn values that required further evaluation (8/18, 44%). Institutions using the hs-cTnI assay favored sex-specific cutoffs (5/6, 83%), whereas institutions employing the hs-cTnT assay favored a combined cutoff (12/12, 100%). Timing of serial hs-cTn measurements varied, with 0-3 hours (8/17, 47%) most frequent, followed by 0-2 hours (4/17, 24%), 0-1 hour (3/17, 18%), and other (2/17, 12%).ConclusionsOur survey of hs-cTn implementation at international institutions reveals satisfaction with new assays but reflects important variations in clinical practice. The use of sex-specific vs. combined cutoffs and timing of serial hs-cTn measurements varies across institutions and are subjects that United States centers must define without consensus from international practices

Comparative performance of COVID-19 serology testing.

BackgroundThe 2019 novel coronavirus infectious disease (COVID-19) pandemic resulted in a surge of assays aimed at detecting severe acute respiratory syndrome (SARS) - coronavirus (CoV) - 2 infection and prior exposure. Although both molecular and antigen testing have clearly defined uses, the utility of serology remains uncertain and is presently not recommended for assessing immunity.MethodsWe conducted a pragmatic, observational study evaluating four commercially available emergency use authorized laboratory-based COVID-19 serology assays (Assays A-D). Remnant samples from hospitalized, and non-hospitalized SARS-CoV-2 PCR positive patients, as well as vaccinated and unvaccinated individuals were collected and tested. Positive percent agreement (PPA) and negative percent agreement (NPA) were calculated. Antibody concentrations were compared across the platforms and populations.ResultsA total of 588 remnant samples derived from 500 patients were tested. PPA at 5-12 weeks post-PCR positive results for Assays A-D was 98.3, 97.4, 99.2, and 95.8% respectively. NPA was 100% across all platforms. Mean antibody concentrations at 2-4 weeks post-PCR positive result were significantly higher in hospitalized versus non-hospitalized patients, respectively, for Assay A (131.8 [101.7] vs. 95.6 [100.3] AU/mL, P < 0.001), B (61.7 [62.4] vs. 38.1 [40.5] AU/mL, P < 0.001), and C (157.6 [105.3] vs. 133.3 [100.7] AU/mL, P < 0.001). For individuals receiving two vaccine doses mean antibody concentrations were respectively 169.6 (104.4), 27.3 (50.8), 189.6 (120.9), 21.19 (13.1) AU/mL for Assays A-D.ConclusionsOverall, PPA and NPA differed across the four assays. Assays A and C produced higher PPA and NPA and detected larger concentrations of antibodies following vaccination